Media Summary: The Johns Hopkins University Applied Mathematics & Statistics Department Seminar series from Thursday September 21st 2017 ... Stefanie Jegelka, MIT Foundations of Machine ... Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ...

On Optimizing A Submodular Utility - Detailed Analysis & Overview

The Johns Hopkins University Applied Mathematics & Statistics Department Seminar series from Thursday September 21st 2017 ... Stefanie Jegelka, MIT Foundations of Machine ... Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ... This videos from ICSI660 class in 12/03/2018. The professor is Feng Chen. He comes from University at Albany, State University ... Niv Buchbinder, Tel Aviv University Discrete In this lecture we consider the problem of maximizing a monotone

The study of combinatorial problems with a Machine Learning Work Shop - Session 2 - Jeff Bilmes - 'Why Vahab Mirrokni, Google Learning, Algorithm Design and Beyond ...

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On optimizing a submodular utility function Part1
On optimizing a submodular utility function Part2
Submodularity and Optimization -- Jeff Bilmes (Part 1)
Submodularity: Theory and Applications I
MIT 6.854 Spring 2016 Lecture 13: Submodular Functions
Submodularity and Optimization -- Jeff Bilmes (Part 2)
Submodular Optimization and Machine Learning - Part 1
Submodular Optimization 1
Submodular Optimization
10.3 Submodular Functions, Part III
Continuous Methods for Submodular Maximization
Machine Learning Work Shop  - Why Submodularity is Important to Machine Learning
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On optimizing a submodular utility function Part1

On optimizing a submodular utility function Part1

The Johns Hopkins University Applied Mathematics & Statistics Department Seminar series from Thursday September 21st 2017 ...

On optimizing a submodular utility function Part2

On optimizing a submodular utility function Part2

The Johns Hopkins University Applied Mathematics & Statistics Department Seminar series from Thursday September 21st 2017 ...

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Submodularity and Optimization -- Jeff Bilmes (Part 1)

Intro ...

Submodularity: Theory and Applications I

Submodularity: Theory and Applications I

Stefanie Jegelka, MIT https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-1 Foundations of Machine ...

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

MIT 6.854 Spring 2016 Lecture 13: Submodular Functions

Recorded by Andrew Xia 2016.

Submodularity and Optimization -- Jeff Bilmes (Part 2)

Submodularity and Optimization -- Jeff Bilmes (Part 2)

... within this particular

Submodular Optimization and Machine Learning - Part 1

Submodular Optimization and Machine Learning - Part 1

Many problems in machine learning that involve discrete structures or subset selection may be phrased in the language of ...

Submodular Optimization 1

Submodular Optimization 1

This videos from ICSI660 class in 12/03/2018. The professor is Feng Chen. He comes from University at Albany, State University ...

Submodular Optimization

Submodular Optimization

Niv Buchbinder, Tel Aviv University https://simons.berkeley.edu/talks/niv-buchbinder-09-13-17 Discrete

10.3 Submodular Functions, Part III

10.3 Submodular Functions, Part III

In this lecture we consider the problem of maximizing a monotone

Continuous Methods for Submodular Maximization

Continuous Methods for Submodular Maximization

The study of combinatorial problems with a

Machine Learning Work Shop  - Why Submodularity is Important to Machine Learning

Machine Learning Work Shop - Why Submodularity is Important to Machine Learning

Machine Learning Work Shop - Session 2 - Jeff Bilmes - 'Why

Sketching and Randomization for Distributed Submodular and Coverage Optimization

Sketching and Randomization for Distributed Submodular and Coverage Optimization

Vahab Mirrokni, Google https://simons.berkeley.edu/talks/vahab-mirrokni-2016-11-17 Learning, Algorithm Design and Beyond ...